05_Bin5000_Processing
library(rmarkdown)
library(tidyverse)## -- Attaching packages ---------------------------------------------------------------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.4 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0## -- Conflicts ------------------------------------------------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(Seurat)## Registered S3 method overwritten by 'spatstat':
## method from
## print.boxx clilibrary(Signac)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2library(ggplot2)
library(GenomeInfoDb)## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
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## anyDuplicated, append, as.data.frame, basename, cbind, colnames,
## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which.max, which.min## Loading required package: S4Vectors## Loading required package: stats4##
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## reducelibrary(patchwork)
set.seed(1234)
nproc=20
library(future)##
## Attaching package: 'future'## The following object is masked from 'package:IRanges':
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## runoptions(future.globals.maxSize = 50 * 1024 ^ 3) # for 50 Gb RAM
plan("multiprocess", workers = nproc)
#plan("sequential") #for Rstudiosource(file = here::here("00_parameters.R"))
getwd()## [1] "/nfsdata/projects/jph712/cutntag_analysis/H3K27ac_VMH_ExptD"source(file = paste0(projPath,"/00_Rfunctions.R"))
library(BSgenome.Mmusculus.UCSC.mm10)## Loading required package: BSgenome## Loading required package: GenomicRanges## Loading required package: Biostrings## Loading required package: XVector##
## Attaching package: 'XVector'## The following object is masked from 'package:purrr':
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## compact##
## Attaching package: 'Biostrings'## The following object is masked from 'package:base':
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## strsplit## Loading required package: rtracklayermetadata<-readRDS(paste0(projPath,"/", ExptName, "_metadataObject.RDS"))
fragments <- CreateFragmentObject(
path = paste0(outsPath, "/fragments.tsv.gz"),
cells=metadata %>% filter(is__cell_barcode==1) %>% pull(barcode),
validate.fragments = FALSE
)## Computing hash### Add GenomeBinMatrix
#add new counts based on bins:
bin_matrix <- GenomeBinMatrix(
fragments = fragments,
genome = seqlengths(BSgenome.Mmusculus.UCSC.mm10::BSgenome.Mmusculus.UCSC.mm10),
binsize = 5000,
cells=metadata %>% filter(is__cell_barcode==1) %>% pull(barcode)
)## Extracting reads overlapping genomic regionsdim(bin_matrix)## [1] 545114 652bin_matrix<-bin_matrix[which(rowSums(bin_matrix)>10),] #doesn't work in the createSeuratObject?
dim(bin_matrix)## [1] 363994 652bin_matrixAssay<- CreateChromatinAssay(
counts = bin_matrix,
sep = c(":", "-"),
fragments = paste0(outsPath, "/fragments.tsv.gz"),
genome = 'mm10'
)## Computing hashsignac <- CreateSeuratObject(
counts = bin_matrixAssay,
assay = "bin5000",
min.cells = 1
)
signac<-AddMetaData(signac, metadata %>% column_to_rownames("barcode"))Annotate and save RDS object:
#See script to create annotations in /nfsdata/projects/jph712/CUTnTag/
annotations<-readRDS("/scratch/kristian/scATAC/signac/EnsDb.Mmusculus.v79_getRangesAnnotation.RDS")
# add the gene information to the object
Annotation(signac) <- annotations
signac<-NucleosomeSignal(signac)gene.activities <- GeneActivity(signac)## Extracting gene coordinates## Extracting reads overlapping genomic regions# add the gene activity matrix to the Seurat object as a new assay and normalize it
signac[['RNA']] <- CreateAssayObject(counts = gene.activities)## Warning: Non-unique features (rownames) present in the input matrix, making
## uniquesignac <- NormalizeData(
object = signac,
assay = 'RNA',
normalization.method = 'LogNormalize',
scale.factor = median(signac$nCount_RNA)
)## Warning: UNRELIABLE VALUE: Future ('future_lapply-1') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-2') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-3') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-4') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-5') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-6') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-7') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-8') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-9') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-10') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-11') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-12') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-13') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-14') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-15') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-16') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-17') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-18') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-19') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".## Warning: UNRELIABLE VALUE: Future ('future_lapply-20') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".saveRDS(signac, paste0(projPath,"/H3K27ac_Bin5000_SingletsObjectNoAdditionalFiltering.RDS"))
signac## An object of class Seurat
## 385972 features across 652 samples within 2 assays
## Active assay: bin5000 (363994 features, 0 variable features)
## 1 other assay present: RNAAdditional filtering:
VlnPlot(signac, c("nucleosome_signal", "nFeature_bin5000"), log=T)
signac<-
subset(signac, subset=
nucleosome_signal < 3 &
passed_filters > 10^cutoff_reads_min &
enhancer_region_ratio > cutoff_enhancer_ratio &
passed_filters < 10^cutoff_reads_max
)
signac## An object of class Seurat
## 385972 features across 636 samples within 2 assays
## Active assay: bin5000 (363994 features, 0 variable features)
## 1 other assay present: RNAsaveRDS(signac, paste0(projPath,"/H3K27ac_Bin5000_SingletsObject.RDS"))Dimensionality reduction
Find top features from bins-> TF-IDF, SVD, UMAP
DefaultAssay(signac) <- 'bin5000'
signac <- FindTopFeatures(signac, min.cutoff = "q5",verbose = T)
#readRDS atac to get top features.
signac## An object of class Seurat
## 385972 features across 636 samples within 2 assays
## Active assay: bin5000 (363994 features, 347061 variable features)
## 1 other assay present: RNAsignac <- RunTFIDF(signac)## Performing TF-IDF normalizationsignac <- RunSVD(signac,
assay = 'bin5000',
reduction.key = 'LSIbin_',
reduction.name = 'lsi_')## Running SVD## Scaling cell embeddingsDepthCor(signac, reduction = "lsi_",n = ndim)
RunUMAP
Excluding dimension #1 since it correlates with seq depth.
# signac <- RunUMAP(signac, reduction = 'lsi_', dims = 2:30, reduction.name = 'lsi_umap', n.start=1000,spread=0.5, min_dist=0.001, n.neighbors = 30)
signac <- RunUMAP(signac, reduction = 'lsi_', dims =c(2:ndim), reduction.name = 'lsi_umap')## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 15:23:13 UMAP embedding parameters a = 0.9922 b = 1.112## 15:23:13 Read 636 rows and found 29 numeric columns## 15:23:13 Using Annoy for neighbor search, n_neighbors = 30## 15:23:14 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 15:23:14 Writing NN index file to temp file /tmp/Rtmphi7uJ9/file3b1963452112
## 15:23:14 Searching Annoy index using 20 threads, search_k = 3000
## 15:23:14 Annoy recall = 100%
## 15:23:16 Commencing smooth kNN distance calibration using 20 threads
## 15:23:20 Initializing from normalized Laplacian + noise
## 15:23:20 Commencing optimization for 500 epochs, with 25128 positive edges
## 15:23:24 Optimization finishedDimPlot(signac, reduction = 'lsi_umap', cols=c("polychrome"), label =T) +
NoLegend() +
ggtitle("LSIbin_UMAP")
# ggsave("dimPlot_bin_lsi.png")
FeaturePlot(signac, features=c("nCount_bin5000"),reduction = "lsi_umap" )
FeaturePlot(signac, features=c("nFeature_bin5000"),reduction = "lsi_umap" )
FeaturePlot(signac, features=c("passed_filters"),reduction = "lsi_umap" )
#ggsave("dimPlot_bin_lsi_TSS.percentile.png")DefaultAssay(signac) <- 'bin5000'
signac <- FindNeighbors(object = signac, reduction = 'lsi_', dims =c(2:ndim), force.recalc = T)## Computing nearest neighbor graph## Computing SNNsignac <- FindClusters(object = signac, verbose = FALSE, algorithm = 3, resolution=0.4)## Warning: UNRELIABLE VALUE: Future ('future_lapply-1') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore".table(signac@meta.data$seurat_clusters)##
## 0 1 2 3
## 367 132 95 42p_clusters<-DimPlot(signac, reduction = 'lsi_umap', label =T, label.box = T, repel = T) +ggtitle("Clusters based on LSI bin5000")+NoLegend()
p_clusters
# p_dimplotSamples<-DimPlot(signac, reduction = 'lsi_umap', group.by="hash.ID", label =F, label.box = F, repel = T) +ggtitle("Samples based on LSI bin5000")
# p_dimplotSamplesVlnPlot(signac,
features = c("nCount_bin5000",
"nFeature_bin5000",
"nCount_HTO",
"passed_filters",
#"mitochondrial",
"enhancer_region_ratio",
"nCount_RNA",
"nFeature_RNA",
# "HTO_margin",
"blacklist_region_fragments",
"nucleosome_signal"
)#, split.by = "is_cellranger_cell_barcode"
)
table(signac$seurat_clusters, signac$is_cellranger_cell_barcode)##
## 0 1
## 0 71 296
## 1 3 129
## 2 2 93
## 3 1 41Make new bam-file for with only called cells
R script to print filtered_cell_barcodes
cells<-
signac@meta.data %>%
#as_tibble() %>%
rownames_to_column("barcode") %>%
filter(is__cell_barcode=="1") %>%
dplyr::select(barcode, seurat_clusters)
cells$seurat_clusters<-paste0("BinCluster",cells$seurat_clusters)
write_tsv(x = cells, file=paste0(projPath,"/bam/FilteredCellBarcodes_BinClusters.tsv"), col_names = FALSE)Save RDS object:
saveRDS(signac, "H3K27ac_BinClusteredObject.RDS")markers<-
c(
"Agrp", "Npy", "Sst",
"Pomc",
"Meis2", "Tbr1", "Zfhx4",
"Nr5a1", "Ghrh", "Otp", "Oxt",
"Cck", "Ebf3",
"Slc32a1", "Slc17a6",
"Gfap",
"Satb2"
)
DefaultAssay(signac) <- 'RNA'
p_markers<-FeaturePlot(
object = signac,
features = c(markers, "nCount_bin5000"),
pt.size = 0.1,
max.cutoff = 'q90',
ncol = floor(length(markers)/5)
) & NoLegend() & NoAxes()
p_markers
p_vln_markers<-VlnPlot(signac, features=markers, group.by="seurat_clusters")
p_vln_markers
DefaultAssay(signac) <- 'bin5000'
da_bins <- FindAllMarkers(
object = signac,
min.diff.pct = 0.1,
logfc.threshold = 0.1,
test.use = 'LR'
)
da_binsGR <- makeGRangesFromDataFrame(separate(da_bins, col=gene, sep="-", c("Chr", "Start", "End")))
elementMetadata(da_binsGR)<-da_bins
blacklist.df = read_tsv(file="/projects/jph712/CUTnTag/H3K27ac_ARC_scCUTnTAG_B/deepTools/mm10-blacklist.v3.bed" ,col_names = F)
blacklist.gr=GenomicRanges::GRanges(seqnames=blacklist.df$X1,
ranges=IRanges(start=as.numeric(blacklist.df$X2),
end=as.numeric(blacklist.df$X3)))
simpleGR_diff<-GenomicRanges::setdiff(da_binsGR, blacklist.gr, ignore.strand=TRUE)
da_binsGR_white<-subsetByOverlaps(da_binsGR,simpleGR_diff, ignore.strand=TRUE)
filtered_DAbins.df<-
da_binsGR_white@elementMetadata %>%
as_tibble() %>%
filter(p_val_adj<0.1 & avg_logFC>0.25 |p_val_adj<0.1 & -0.25>avg_logFC) %>%
distinct(gene, .keep_all=T) %>%
group_by(cluster) %>%
arrange(avg_logFC) %>%
dplyr::select(gene, cluster, avg_logFC,p_val_adj, everything())
########
closest <- ClosestFeature(signac,
regions = filtered_DAbins.df$gene,
)
filtered_DAbins.df<-left_join(filtered_DAbins.df, closest, by=c("gene"= "query_region"))
#write DA bins to excel file
writexl::write_xlsx(filtered_DAbins.df, path = "DiffAcetylatedBINS_FindAll.xlsx", col_names = TRUE)go to chapter 06.
sessionInfo()## R version 4.0.2 (2020-06-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.so
##
## locale:
## [1] LC_CTYPE=C LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] BSgenome.Mmusculus.UCSC.mm10_1.4.0 BSgenome_1.58.0
## [3] rtracklayer_1.50.0 Biostrings_2.58.0
## [5] XVector_0.30.0 GenomicRanges_1.42.0
## [7] future_1.20.1 patchwork_1.1.0
## [9] GenomeInfoDb_1.26.2 IRanges_2.24.1
## [11] S4Vectors_0.28.1 BiocGenerics_0.36.0
## [13] Signac_1.1.0 Seurat_3.2.2
## [15] forcats_0.5.0 stringr_1.4.0
## [17] dplyr_1.0.2 purrr_0.3.4
## [19] readr_1.4.0 tidyr_1.1.2
## [21] tibble_3.0.4 ggplot2_3.3.2
## [23] tidyverse_1.3.0 rmarkdown_2.5
##
## loaded via a namespace (and not attached):
## [1] rappdirs_0.3.1 SnowballC_0.7.0
## [3] GGally_2.0.0 bit64_4.0.5
## [5] knitr_1.30 irlba_2.3.3
## [7] DelayedArray_0.16.0 data.table_1.13.4
## [9] rpart_4.1-15 RCurl_1.98-1.2
## [11] AnnotationFilter_1.14.0 generics_0.1.0
## [13] GenomicFeatures_1.42.1 cowplot_1.1.0
## [15] RSQLite_2.2.1 RANN_2.6.1
## [17] bit_4.0.4 spatstat.data_1.5-2
## [19] xml2_1.3.2 lubridate_1.7.9.2
## [21] httpuv_1.5.4 SummarizedExperiment_1.20.0
## [23] assertthat_0.2.1 xfun_0.19
## [25] hms_0.5.3 evaluate_0.14
## [27] promises_1.1.1 fansi_0.4.1
## [29] progress_1.2.2 dbplyr_2.0.0
## [31] readxl_1.3.1 igraph_1.2.6
## [33] DBI_1.1.0 htmlwidgets_1.5.2
## [35] reshape_0.8.8 ellipsis_0.3.1
## [37] RSpectra_0.16-0 backports_1.2.0
## [39] biomaRt_2.46.0 deldir_0.2-3
## [41] MatrixGenerics_1.2.0 vctrs_0.3.5
## [43] Biobase_2.50.0 here_1.0.0
## [45] ensembldb_2.14.0 ROCR_1.0-11
## [47] abind_1.4-5 withr_2.3.0
## [49] ggforce_0.3.2 checkmate_2.0.0
## [51] sctransform_0.3.1 GenomicAlignments_1.26.0
## [53] prettyunits_1.1.1 goftest_1.2-2
## [55] cluster_2.1.0 lazyeval_0.2.2
## [57] crayon_1.3.4 pkgconfig_2.0.3
## [59] labeling_0.4.2 tweenr_1.0.1
## [61] nlme_3.1-150 ProtGenerics_1.22.0
## [63] nnet_7.3-14 rlang_0.4.9
## [65] globals_0.14.0 lifecycle_0.2.0
## [67] miniUI_0.1.1.1 BiocFileCache_1.14.0
## [69] modelr_0.1.8 rsvd_1.0.3
## [71] dichromat_2.0-0 cellranger_1.1.0
## [73] rprojroot_2.0.2 polyclip_1.10-0
## [75] matrixStats_0.57.0 lmtest_0.9-38
## [77] graph_1.68.0 Matrix_1.2-18
## [79] ggseqlogo_0.1 zoo_1.8-8
## [81] reprex_0.3.0 base64enc_0.1-3
## [83] ggridges_0.5.2 png_0.1-7
## [85] viridisLite_0.3.0 bitops_1.0-6
## [87] KernSmooth_2.23-18 blob_1.2.1
## [89] parallelly_1.21.0 jpeg_0.1-8.1
## [91] scales_1.1.1 memoise_1.1.0
## [93] magrittr_2.0.1 plyr_1.8.6
## [95] ica_1.0-2 zlibbioc_1.36.0
## [97] compiler_4.0.2 RColorBrewer_1.1-2
## [99] fitdistrplus_1.1-3 Rsamtools_2.6.0
## [101] cli_2.2.0 listenv_0.8.0
## [103] pbapply_1.4-3 ps_1.5.0
## [105] htmlTable_2.1.0 Formula_1.2-4
## [107] MASS_7.3-53 mgcv_1.8-33
## [109] tidyselect_1.1.0 stringi_1.5.3
## [111] yaml_2.2.1 askpass_1.1
## [113] latticeExtra_0.6-29 ggrepel_0.8.2
## [115] grid_4.0.2 VariantAnnotation_1.36.0
## [117] fastmatch_1.1-0 tools_4.0.2
## [119] future.apply_1.6.0 rstudioapi_0.13
## [121] foreign_0.8-80 lsa_0.73.2
## [123] gridExtra_2.3 farver_2.0.3
## [125] Rtsne_0.15 digest_0.6.27
## [127] BiocManager_1.30.10 shiny_1.5.0
## [129] Rcpp_1.0.5 broom_0.7.2
## [131] later_1.1.0.1 RcppAnnoy_0.0.17
## [133] OrganismDbi_1.32.0 httr_1.4.2
## [135] AnnotationDbi_1.52.0 ggbio_1.38.0
## [137] biovizBase_1.38.0 colorspace_2.0-0
## [139] rvest_0.3.6 XML_3.99-0.5
## [141] fs_1.5.0 tensor_1.5
## [143] reticulate_1.18 splines_4.0.2
## [145] uwot_0.1.9 RBGL_1.66.0
## [147] RcppRoll_0.3.0 spatstat.utils_1.17-0
## [149] plotly_4.9.2.1 xtable_1.8-4
## [151] jsonlite_1.7.2 spatstat_1.64-1
## [153] R6_2.5.0 Hmisc_4.4-2
## [155] pillar_1.4.7 htmltools_0.5.0
## [157] mime_0.9 glue_1.4.2
## [159] fastmap_1.0.1 BiocParallel_1.24.1
## [161] codetools_0.2-18 lattice_0.20-41
## [163] curl_4.3 leiden_0.3.6
## [165] openssl_1.4.3 survival_3.2-7
## [167] munsell_0.5.0 GenomeInfoDbData_1.2.4
## [169] haven_2.3.1 reshape2_1.4.4
## [171] gtable_0.3.0